From the calculator to the computer, new innovations have always brought on anxiety about the human workers they might displace—but time and time again, innovation has resulted in an explosion of technical jobs that leverage these new tools.
Generative AI has been in the headlines for months now, with much hand-wringing about how these technologies will upend the job market. Despite the doom and gloom about job displacement, we find ourselves at historically low unemployment. And in fact, some studies have found that the industries most exposed to AI disruption have seen an uptick in employment in recent years.
We believe that while some rote tasks will increasingly be automated, AI innovations will also open up new areas of opportunity across the workforce. Vitally, understanding these new technologies will be paramount to the jobs of the future. In the words of IBM Chief Commercial Officer Rob Thomas, “AI may not replace managers, but the managers that use AI will replace the managers that do not.”
Below, we dig into what AI in the workplace will look like today, in the near term, and in the distant future—and the questions companies should be asking to prepare for this sea change.
Are you a founder innovating in the AI space? We want to talk. Drop our investing team a line at firstname.lastname@example.org or email@example.com.
Careers of today, tomorrow, and the next generation
What does AI in the workplace look like? We dive into how AI tools will impact job functions across industries, from engineers to designers, across three distinct time horizons.
Zero to little job replacement. AI cannot fully replace human workers, but generative models can augment certain tasks.
We begin to see some entry-level job replacement as AI models become better at completing certain rote tasks.
As AI replaces more jobs, markets will require new human opportunities and jobs will emphasize strategy and management skills.
A junior engineer uses AI tools to write simple code, then makes adjustments to the generated code as needed.
A senior engineer steers an AI tool with prompts, but makes fewer adjustments to the outputs (work mostly happens at the input stage).
A single head of engineering runs a “team” of AI models, which are able to commission their own prompts to write code based on an end product vision.
A junior SDR uses generative AI to draft emails to prospects, then adds in additional details and warmth.
A senior SDR finalizes deals and provides a human touch, using an AI sales co-pilot to initiate early conversations with prospects. Time is freed up for a human rep to focus on the most valuable clients without neglecting other prospects.
A sales professional focuses on deeper human-to-human connections. An AI agent or sales rep clone with a realistic, human-like interface manages most interactions with prospects, from outreach to RFP to answering questions.
A designer uses generative AI as an ideation generator to brainstorm a marketing campaign, e.g. “Everyday Superheroes in the style of Wes Anderson with our brand’s color palette.”
A designer creates a key creative asset and leverages AI to create dozens of variations for different use cases and media formats. This allows them to focus on creative work and use AI to automate highly repetitive tasks in their workflow.
A designer can personalize marketing assets for every user—for example, a furniture company’s visuals will feature regionally specific foods on a table or views through a window, generated by an AI model.
A doctor uses generative AI to summarize clinical decision support. Healthcare administration is augmented with support around care coordination (call centers) and data input associated with prior authorization, coding/billing, and patient intake.
A doctor uses generative AI to recommend clinical pathways and medications based on a patient's personalized history. Co-pilots exist for patient intake, paperwork, and revenue cycle management (RCM) tasks.
A doctor uses AI tools to automate the full admin cycle, from document intake to billing and insurance reimbursement. A patient’s first call goes to an AI agent with diagnostic capabilities, and specific human doctors step in later. Robots may automate or augment certain surgical procedures.
An accountant uses generative AI to summarize tax and accounting regulation that would have normally required hours of research.
An accountant can train client models on their financial data and use AI as a co-pilot to draft financial statements, though a human worker will need to check the AI's work and interpret gray zones.
Senior tax officials interpret new legislation and provide final reviews as accounting and tax filings become fully automated with AI agents.
A talent intern uses generative AI to outline and edit job descriptions.
A senior talent coordinator writes prompts to use an AI model to source candidates, freeing up time to focus on the human relationship building component of recruiting.
A human head of talent makes final decisions, while an AI model automates recruiting, sourcing, evaluating, and communicating with candidates and organizes the interview process.
A merchandiser uses AI to run inventory simulations for the upcoming season, then creates an inventory plan.
A merchandiser relies on an AI inventory planning model that constantly adjusts to real-time sales data to balance inventory across channels.
A head of merchandising uses an AI merchandising agent to lead product planning and inventory planning across channels, place new manufacturing orders, and route existing inventory in response to real-time sales.
What are the implications of this technology?
The impact of AI will evolve alongside its technological progress, with important ramifications for individual workers, team managers, and the workforce at large. Here are some to keep in mind.
AI will not directly replace many human workers, but the ability to enhance work productivity might limit the need for increases in support staff.
“Prompt engineer” will become an important role for a period of time.
We will begin to see temporary job displacement as old opportunities disappear and new ones have yet to fully emerge.
There will be an increased focus on how to manage AI and how to explain AI outcomes.
We will see larger-scale temporary job displacement, as workers replaced by AI settle into a new market.
Personalization and automation will level the playing field and companies will need to differentiate on strategy and product.
Service industry and experiential jobs will expand, as they require a human touch.
Decision validation and accountability may be the last bastion of human work.
What questions should companies be asking?
Smart companies need to ask smart questions in order to prepare for the impact of AI in the short and long term.
What AI tools should employees be getting familiar with now?
What rote tasks can AI help expedite?
As junior positions become more automated, how can you help workers grow to reach senior positions?
What support is needed for the transition as some old jobs are automated and new roles are needed?
What LLM infrastructure will be required to provide testing, observability, and explainability features needed to judge model output quality?
What skills will be needed to manage this transition? How do we train our workers today to be ready for tomorrow, especially given the expected shortage of these skills in the marketplace?
What uniquely human tasks will humans focus on? How can we double down on investing in these areas to get the best out of new time and focus here?
What new categories of jobs will be unlocked by AI technology? Where will strategy and management become even more critical?
What regulatory guardrails do we need to be aware of as the use of AI expands? (For example, to avoid a chatbot illegally giving medical or legal advice.)
If there is a shortage of jobs, how will this impact society? What benefits will workers or non-workers need?
Industries to watch
What key industries will be disrupted? (Other than all of them.) At M13, we invest in the future of work, money, health, and commerce. Some of the verticals across these categories that will see AI-driven disruption include:
- Game design automation
- AI-driven characters and gaming assets
- Animation generation and voice generation
- Video production and editing
Art & design
- Web and mobile application design and development
- Architectural design
- Clothing design
- Music generation
- Art generation
- Creative writing
Sales & marketing
- Prospecting and outbound
- RFP process
- Generating personalized content for marketing segments
- Generating marketing assets
- Summarizing product improvements
- Voice and visual sales clones
Retail & commerce
- Inventory management
- Demand generation
- Dynamic pricing
- Customer service chatbot
- Disease diagnosis
- Personalized clinical decision support / care plans
- Documentation admin (e.g., prior authorization)
- RCM automation (e.g., coding and billing)
- Automated care delivery
- Candidate screening and prospecting
- Job description and interview automation
- Training and development
- Performance analytics automation
- Fraud detection
- Financial advising
- Automated tax filings
- Research summarization
- Contract drafting
- Discovery process automation
- Automated negotiation
- Outcome prediction
General work productivity
- Automating customer research
- Presentation generation
- Automating business intelligence and summarizing conclusions
- Personal assistant for email, scheduling, web actions
- Generative networking agent for managing relationships
The future of human work
While recent headlines have focused on potential job loss due to the adoption of AI, the World Economic Forum forecasted that the advent of automation will actually create 58 million highly skilled jobs over the next few years—and add 5% to the US GDP. As with prior innovation cycles, strategic leadership work will become even more important.
As humans are freed up from all of this paper pushing, productivity-oriented stuff, we're going to be freed up to do things that only humans can do.
Adobe Chief Strategy Officer Scott Belsky at M13’s Future Perfect 2023
Accountability: Companies will need human leaders to be the public face of the company and take responsibility internally and externally for company actions.
Strategy: Deciding what to do, why, and for whom will remain in the purview of human leadership for the foreseeable future. Like any other machine or system, the outputs are only as good as the inputs—and what we ask AI to do will be within the bounds of human objectives and desires.
Mentorship & coaching: While AI tutors will be able to teach us countless skills, we think that higher level mentorship on how to be human will remain the purview of humans. There are some conversations for which a live human will always be the best option.
Relationships: In an area when so much “transactional” work can be done by AI, human relationships will take on even more importance. Human-to-human connections will stand out in a world in which many everyday tasks are done by AI agents.